Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Poster Abstract: MagoNode++ - A Wake-Up-Radio-Enabled Wireless Sensor Mote for Energy-Neutral Applications

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications

Wake-up radio-based data forwarding for green wireless networks

This paper presents G-WHARP, for Green Wake-up and HARvesting-based energy-Predictive forwarding, a wake-up radio-based forwarding strategy for wireless networks equipped with energy harvesting capabilities (green wireless networks). Following a learning-based approach, G-WHARP blends energy harvesting and wake-up radio technology to maximize energy efficiency and obtain superior network performance. Nodes autonomously decide on their forwarding availability based on a Markov Decision Process (MDP) that takes into account a variety of energy-related aspects, including the currently available energy and that harvestable in the foreseeable future. Solution of the MDP is provided by a computationally light heuristic based on a simple threshold policy, thus obtaining further computational energy savings. The performance of G-WHARP is evaluated via GreenCastalia simulations, where we accurately model wake-up radios, harvestable energy, and the computational power needed to solve the MDP. Key network and system parameters are varied, including the source of harvestable energy, the network density, wake-up radio data rate and data traffic. We also compare the performance of G-WHARP to that of two state-of-the-art data forwarding strategies, namely GreenRoutes and CTP-WUR. Results show that G-WHARP limits energy expenditures while achieving low end-to-end latency and high packet delivery ratio. Particularly, it consumes up to 34% and 59% less energy than CTP-WUR and GreenRoutes, respectively

10 Women in Networking/Communications That You Should Watch

http://n2women.comsoc.org/10-women-in-networkingcommunications-that-you-should-know/2016-10-women-in-networkingcommunications-that-you-should-watch

Visita presso Università estera: Penn State University

Hosted by Prof. Thomas La Porta, Department of Computer Science and Engineerin

Google European Doctoral Fellowship in Wireless Networking

The Google European Doctoral Fellowship Programme was created to support outstanding doctoral students doing exceptional research in Computer Science or closely related areas

CTP-WUR: the collection Tree Protocol in Wake-up Radio WSNs for critical applications

Allowing the nodes of a wireless sensor network (WSN) to turn their radio off periodically noticeably increases network lifetime. Duty cycling, however, does not eliminate idle listening, comes at the price of longer latencies and obtains lifetimes that are still insufficient for many critical applications. Using a wake-up receiver (WUR) allows actual communications on the main radio only for transmission or reception, virtually eliminating node idling. However, the range of current WUR prototypes is still significantly shorter than that of the main radio, which can challenge the use of existing WSN protocols in WUR-based networks. In this paper we present an approach to mitigate this limitation of wake-up-based networks. In particular, we show that the Collection Tree Protocol (CTP), a standard protocol for data gathering in WSNs, suitably redefined to work on WUR-endowed nodes, achieves lifetimes of several decades. This constitutes a remarkable improvement over duty cycle-based solutions, where CTP makes the network lasts only a handful of months. At the same time, our WUR-based approach obtains data latencies comparable to those obtained by keeping the main radio always on

Improving energy predictions in EH-WSNs with Pro-Energy-VLT

The increasing popularity of micro-scale energy-scavenging techniques for wireless sensor networks (WSNs) is opening new opportunities for the development of energy-autonomous systems. To sustain perpetual operations, however, environmentally-powered motes must adapt their workload to the stochastic nature of ambient power sources. Energy prediction algorithms, which forecast the source availability and estimate the expected energy intake in the near future, are precious tools to support the development of proactive power management strategies. In this work, we propose Pro-Energy-VLT, an enhancement of the Pro-Energy prediction algorithm that improves the accuracy of energy predictions, while reducing its memory and energy overhead

GreenCastalia: An energy-harvesting-enabled framework for the Castalia simulator

The emergence of energy-scavenging techniques for powering networks of embedded devices is raising the need for dedicated simulation frameworks that can support researchers and developers in the design and performance evaluation of harvesting-aware protocols and algorithms. In this work we present GreenCastalia, an open-source energy-harvesting simulation framework we have developed for the popular Castalia simulator. GreenCastalia supports multi-source and multi-storage energy harvesting architectures, it is highly modular and easily customizable. In addition, it allows to simulate networks of embedded devices with heterogeneous harvesting capabilities. © 2013 ACM.The emergence of energy-scavenging techniques for powering networks of embedded devices is raising the need for dedicated simulation frameworks that can support researchers and developers in the design and performance evaluation of harvesting-aware protocols and algorithms. In this work we present GreenCastalia, an open-source energy-harvesting simulation framework we have developed for the popular Castalia simulator. GreenCastalia supports multi-source and multi-storage energy harvesting architectures, it is highly modular and easily customizable. In addition, it allows to simulate networks of embedded devices with heterogeneous harvesting capabilities